Update app.py

app.py

@@ -10,262 +10,82 @@ from typing import List, Dict, Tuple
 from sklearn.metrics.pairwise import cosine_similarity
 from sentence_transformers import SentenceTransformer
 
-class AGICognitiveSystem:
-    def __init__(self):
-        self.api_keys = {
-            "GEMINI": os.environ.get("GEMINI_API_KEY"),
-            "MISTRAL": os.environ.get("MISTRAL_API_KEY"),
-            "OPENROUTER": os.environ.get("OPENROUTER_API_KEY"),
-            "AZURE": os.environ.get("AZURE_API_KEY")
-        }
-        self.validate_keys()
-        
-        # Initialize models and cognitive components
-        self.init_models()
-        self.init_cognitive_modules()
-        self.init_knowledge_graph()
-        
-        # Initialize sentence transformer for semantic analysis
-        self.sentence_model = SentenceTransformer('all-MiniLM-L6-v2')
-        
-        # Cognitive configuration
-        self.cognitive_config = {
-            "depth": 5,  # Levels of recursive reasoning
-            "temperature_strategy": "adaptive",
-            "confidence_threshold": 0.85,
-            "max_retries": 3,
-            "metacognition_interval": 2
-        }
-        
-        self.thought_history = []
-        self.cognitive_metrics = {
-            "processing_time": [],
-            "confidence_scores": [],
-            "error_rates": []
-        }
-
-    def validate_keys(self):
-        for key, value in self.api_keys.items():
-            if not value:
-                raise ValueError(f"Missing API key: {key}")
-
-    def init_models(self):
-        """Initialize all AI models with specialized roles"""
-        # Google Gemini
-        genai.configure(api_key=self.api_keys["GEMINI"])
-        self.gemini = genai.GenerativeModel(
-            "gemini-2.0-pro-exp-02-05",
-            generation_config={"temperature": 0.5, "max_output_tokens": 8192}
-        )
-        
-        # Azure GPT-4o
-        self.gpt4o = OpenAI(
-            base_url="https://models.inference.ai.azure.com",
-            api_key=self.api_keys["AZURE"]
-        )
-        
-        # Model registry with specialized roles
-        self.model_registry = {
-            "intuition": "mistral-large-latest",
-            "analysis": "gpt-4o",
-            "critique": "meta-llama/llama-3.3-70b-instruct:free",
-            "creativity": "gemini-2.0-pro-exp-02-05",
-            "validation": "deepseek/deepseek-chat:free",
-            "metacognition": "gpt-4o",
-            "emotional_intelligence": "qwen/qwen-vl-plus:free"
-        }
+# Animation CSS and HTML
+LOADING_ANIMATION = """
+<style>
+.thinking-animation {
+    display: flex;
+    justify-content: center;
+    align-items: center;
+    height: 100px;
+}
+
+.dot-flashing {
+    position: relative;
+    width: 10px;
+    height: 10px;
+    border-radius: 5px;
+    background-color: #4CAF50;
+    color: #4CAF50;
+    animation: dotFlashing 1s infinite linear alternate;
+    animation-delay: .5s;
+}
+
+.dot-flashing::before, .dot-flashing::after {
+    content: '';
+    display: inline-block;
+    position: absolute;
+    top: 0;
+}
+
+.dot-flashing::before {
+    left: -15px;
+    width: 10px;
+    height: 10px;
+    border-radius: 5px;
+    background-color: #4CAF50;
+    color: #4CAF50;
+    animation: dotFlashing 1s infinite alternate;
+    animation-delay: 0s;
+}
+
+.dot-flashing::after {
+    left: 15px;
+    width: 10px;
+    height: 10px;
+    border-radius: 5px;
+    background-color: #4CAF50;
+    color: #4CAF50;
+    animation: dotFlashing 1s infinite alternate;
+    animation-delay: 1s;
+}
+
+@keyframes dotFlashing {
+    0% { background-color: #4CAF50; }
+    50%, 100% { background-color: rgba(76, 175, 80, 0.2); }
+}
+
+@keyframes spin {
+    0% { transform: rotate(0deg); }
+    100% { transform: rotate(360deg); }
+}
+
+.thinking-text {
+    text-align: center;
+    margin-top: 20px;
+    font-weight: bold;
+    color: #4CAF50;
+}
+</style>
+
+<div class="thinking-animation">
+    <div class="dot-flashing"></div>
+    <div class="thinking-text">AGI Thinking...</div>
+</div>
+"""
 
-    def init_cognitive_modules(self):
-        """Initialize specialized cognitive processors"""
-        self.modules = {
-            "working_memory": [],
-            "long_term_memory": [],
-            "emotional_context": {"valence": 0.5, "arousal": 0.5},
-            "error_correction": [],
-            "metacognition_stack": []
-        }
-
-    def init_knowledge_graph(self):
-        """Initialize semantic knowledge network"""
-        self.knowledge_graph = {
-            "nodes": [],
-            "edges": [],
-            "embeddings": np.array([])
-        }
-
-    def cognitive_flow(self, query: str) -> Tuple[str, dict]:
-        """Multi-layered cognitive processing pipeline"""
-        try:
-            # Stage 1: Perception & Contextualization
-            context = self.perceive_context(query)
-            
-            # Stage 2: Core Reasoning Process
-            solutions = self.recursive_reasoning(query, context)
-            
-            # Stage 3: Emotional Alignment
-            emotionally_aligned = self.apply_emotional_intelligence(solutions)
-            
-            # Stage 4: Metacognitive Review
-            validated = self.metacognitive_review(emotionally_aligned)
-            
-            # Stage 5: Knowledge Integration
-            self.update_knowledge_graph(query, validated)
-            
-            return validated, {
-                "reasoning_steps": self.thought_history[-5:],
-                "confidence": self.calculate_confidence(validated),
-                "semantic_coherence": self.analyze_coherence(validated)
-            }
-            
-        except Exception as e:
-            self.handle_error(e)
-            return "Cognitive processing failed", {}
-
-    def recursive_reasoning(self, query: str, context: dict, depth: int = 0) -> List[dict]:
-        """Deep recursive reasoning with backtracking"""
-        if depth >= self.cognitive_config["depth"]:
-            return []
-
-        # Generate initial hypotheses
-        hypotheses = self.generate_hypotheses(query, context)
-        
-        # Evaluate hypotheses
-        evaluated = []
-        for hypothesis in hypotheses:
-            analysis = self.analyze_hypothesis(hypothesis, context)
-            critique = self.critique_analysis(analysis)
-            
-            if self.evaluate_critique(critique):
-                refined = self.refine_hypothesis(hypothesis, critique)
-                evaluated.append({
-                    "hypothesis": refined,
-                    "confidence": self.calculate_confidence(refined),
-                    "depth": depth
-                })
-                # Recursive deepening
-                evaluated += self.recursive_reasoning(refined, context, depth+1)
-        
-        return self.rank_solutions(evaluated)
-
-    def generate_hypotheses(self, query: str, context: dict) -> List[str]:
-        """Generate potential solutions using multiple models"""
-        hypotheses = []
-        
-        # Intuitive generation
-        hypotheses.append(self.call_model(
-            "intuition",
-            f"Generate intuitive hypothesis for: {query}",
-            context
-        ))
-        
-        # Analytical generation
-        hypotheses.append(self.call_model(
-            "analysis",
-            f"Generate analytical solution for: {query}",
-            context
-        ))
-        
-        # Creative generation
-        hypotheses.append(self.call_model(
-            "creativity",
-            f"Generate creative approach for: {query}",
-            context
-        ))
-        
-        return [h for h in hypotheses if h]
-
-    def call_model(self, module: str, prompt: str, context: dict) -> str:
-        """Advanced model caller with adaptive temperature and retry"""
-        temperature = self.calculate_temperature(context)
-        retries = 0
-        
-        while retries < self.cognitive_config["max_retries"]:
-            try:
-                if module in ["intuition", "metacognition"]:
-                    return self._call_mistral(prompt, temperature)
-                elif module == "analysis":
-                    return self._call_gpt4o(prompt, temperature)
-                elif module == "creativity":
-                    return self.gemini.generate_content(prompt).text
-                elif module == "emotional_intelligence":
-                    return self._call_qwen(prompt)
-                elif module == "validation":
-                    return self._call_deepseek(prompt)
-                
-            except Exception as e:
-                retries += 1
-                self.handle_error(e)
-        
-        return ""
-
-    def _call_mistral(self, prompt: str, temperature: float) -> str:
-        """Call Mistral API"""
-        headers = {
-            "Authorization": f"Bearer {self.api_keys['MISTRAL']}",
-            "Content-Type": "application/json"
-        }
-        
-        payload = {
-            "model": self.model_registry["intuition"],
-            "messages": [{"role": "user", "content": prompt}],
-            "temperature": temperature,
-            "max_tokens": 2000
-        }
-        
-        response = requests.post(
-            "https://api.mistral.ai/v1/chat/completions",
-            headers=headers,
-            json=payload
-        )
-        
-        return response.json()['choices'][0]['message']['content']
-
-    def _call_gpt4o(self, prompt: str, temperature: float) -> str:
-        """Call GPT-4o via Azure"""
-        try:
-            response = self.gpt4o.chat.completions.create(
-                model=self.model_registry["analysis"],
-                messages=[{"role": "user", "content": prompt}],
-                temperature=temperature,
-                max_tokens=2000
-            )
-            return response.choices[0].message.content
-        except Exception as e:
-            raise RuntimeError(f"GPT-4o Error: {str(e)}")
-
-    def calculate_confidence(self, response: str) -> float:
-        """Calculate semantic confidence score"""
-        query_embed = self.sentence_model.encode(response)
-        knowledge_embeds = self.knowledge_graph["embeddings"]
-        
-        if knowledge_embeds.size == 0:
-            return 0.5  # Neutral confidence
-        
-        similarities = cosine_similarity([query_embed], knowledge_embeds)
-        return np.max(similarities)
-
-    def update_knowledge_graph(self, query: str, response: str):
-        """Dynamic knowledge integration"""
-        embedding = self.sentence_model.encode(response)
-        
-        if self.knowledge_graph["embeddings"].size == 0:
-            self.knowledge_graph["embeddings"] = np.array([embedding])
-        else:
-            self.knowledge_graph["embeddings"] = np.vstack(
-                [self.knowledge_graph["embeddings"], embedding]
-            )
-        
-        self.knowledge_graph["nodes"].append({
-            "id": len(self.knowledge_graph["nodes"]),
-            "content": response,
-            "embedding": embedding.tolist()
-        })
-
-    def handle_error(self, error: Exception):
-        """Error handling and recovery"""
-        self.cognitive_metrics["error_rates"].append(time.time())
-        print(f"System Error: {str(error)}")
-        # Implement error recovery logic here
+class AGICognitiveSystem:
+    # ... (keep previous class implementation unchanged) ...
 
 def create_agi_interface():
     try:
@@ -288,16 +108,35 @@ def create_agi_interface():
                 depth = gr.Slider(1, 10, value=5, label="Reasoning Depth")
                 creativity = gr.Slider(0, 1, value=0.7, label="Creativity Level")
         
+        loading = gr.HTML(LOADING_ANIMATION, visible=False)
         output_panel = gr.Markdown()
         visualization = gr.HTML()
         metrics = gr.DataFrame(headers=["Metric", "Value"])
         
+        def toggle_loading():
+            return gr.HTML(visible=True)
+        
+        def process_query(query):
+            start_time = time.time()
+            result, metrics = agi.cognitive_flow(query)
+            return result, metrics
+        
         input_panel.submit(
-            fn=agi.cognitive_flow,
+            fn=toggle_loading,
+            inputs=None,
+            outputs=loading,
+            queue=False
+        ).then(
+            fn=process_query,
             inputs=input_panel,
-            outputs=[output_panel, metrics]
+            outputs=[output_panel, metrics],
+        ).then(
+            lambda: gr.HTML(visible=False),
+            inputs=None,
+            outputs=loading,
+            queue=False
         )
-        
+
     return demo
 
 if __name__ == "__main__":